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# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Common configurations."""
import dataclasses
from typing import List, Optional
# Import libraries
from official.core import config_definitions as cfg
from official.modeling import hyperparams
@dataclasses.dataclass
class TfExampleDecoder(hyperparams.Config):
"""A simple TF Example decoder config."""
regenerate_source_id: bool = False
mask_binarize_threshold: Optional[float] = None
attribute_names: List[str] = dataclasses.field(default_factory=list)
@dataclasses.dataclass
class TfExampleDecoderLabelMap(hyperparams.Config):
"""TF Example decoder with label map config."""
regenerate_source_id: bool = False
mask_binarize_threshold: Optional[float] = None
label_map: str = ''
@dataclasses.dataclass
class DataDecoder(hyperparams.OneOfConfig):
"""Data decoder config.
Attributes:
type: 'str', type of data decoder be used, one of the fields below.
simple_decoder: simple TF Example decoder config.
label_map_decoder: TF Example decoder with label map config.
"""
type: Optional[str] = 'simple_decoder'
simple_decoder: TfExampleDecoder = dataclasses.field(
default_factory=TfExampleDecoder
)
label_map_decoder: TfExampleDecoderLabelMap = dataclasses.field(
default_factory=TfExampleDecoderLabelMap
)
@dataclasses.dataclass
class RandAugment(hyperparams.Config):
"""Configuration for RandAugment."""
num_layers: int = 2
magnitude: float = 10
cutout_const: float = 40
translate_const: float = 10
magnitude_std: float = 0.0
prob_to_apply: Optional[float] = None
exclude_ops: List[str] = dataclasses.field(default_factory=list)
@dataclasses.dataclass
class AutoAugment(hyperparams.Config):
"""Configuration for AutoAugment."""
augmentation_name: str = 'v0'
cutout_const: float = 100
translate_const: float = 250
@dataclasses.dataclass
class RandomErasing(hyperparams.Config):
"""Configuration for RandomErasing."""
probability: float = 0.25
min_area: float = 0.02
max_area: float = 1 / 3
min_aspect: float = 0.3
max_aspect = None
min_count = 1
max_count = 1
trials = 10
@dataclasses.dataclass
class MixupAndCutmix(hyperparams.Config):
"""Configuration for MixupAndCutmix."""
mixup_alpha: float = .8
cutmix_alpha: float = 1.
prob: float = 1.0
switch_prob: float = 0.5
label_smoothing: float = 0.1
@dataclasses.dataclass
class Augmentation(hyperparams.OneOfConfig):
"""Configuration for input data augmentation.
Attributes:
type: 'str', type of augmentation be used, one of the fields below.
randaug: RandAugment config.
autoaug: AutoAugment config.
"""
type: Optional[str] = None
randaug: RandAugment = dataclasses.field(default_factory=RandAugment)
autoaug: AutoAugment = dataclasses.field(default_factory=AutoAugment)
@dataclasses.dataclass
class NormActivation(hyperparams.Config):
activation: str = 'relu'
use_sync_bn: bool = True
norm_momentum: float = 0.99
norm_epsilon: float = 0.001
@dataclasses.dataclass
class PseudoLabelDataConfig(cfg.DataConfig):
"""Psuedo Label input config for training."""
input_path: str = ''
data_ratio: float = 1.0 # Per-batch ratio of pseudo-labeled to labeled data.
is_training: bool = True
dtype: str = 'float32'
shuffle_buffer_size: int = 10000
cycle_length: int = 10
aug_rand_hflip: bool = True
aug_type: Optional[
Augmentation] = None # Choose from AutoAugment and RandAugment.
file_type: str = 'tfrecord'
# Keep for backward compatibility.
aug_policy: Optional[str] = None # None, 'autoaug', or 'randaug'.
randaug_magnitude: Optional[int] = 10
@dataclasses.dataclass
class TFLitePostProcessingConfig(hyperparams.Config):
"""TFLite Post Processing config for inference."""
max_detections: int = 200
max_classes_per_detection: int = 5
# Regular NMS run in a multi-class fashion and is slow. Setting it to False
# uses class-agnostic NMS, which is faster.
use_regular_nms: bool = False
nms_score_threshold: float = 0.1
nms_iou_threshold: float = 0.5
# Whether to normalize coordinates of anchors to [0, 1]. If setting to True,
# coordinates of output boxes is also normalized but latency increases.
normalize_anchor_coordinates: Optional[bool] = False
# Whether to omit the final nms placeholder op. If set to True, the output
# will be a tuple of boxes, scores result right before the NMS operation.
omit_nms: Optional[bool] = False
# The number of detections per class when using regular NMS.
detections_per_class: Optional[int] = 5
# Box scaling factors. It should agree with `box_coder_weights` defined in
# `DetectionGenerator`, which is in the format of [y, x, w, h].
y_scale: float = 1.0
x_scale: float = 1.0
w_scale: float = 1.0
h_scale: float = 1.0
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